Sensors & Behavior (ARPA-E) > Data Evaluation and Modeling

Social media analytics with Twitter explorer

Understanding conversations in social media could provide program planners and communication managers unique insights into trending thoughts about energy efficiency and climate change. In 140 characters or less, the concerns, interests and public narratives about energy efficiency and climate change can be identified through tweets.

Investigators: Martha Russell, Markus Strohmaier, Jan Pöschko, Rafael Perez, Neil Rubens, June Flora, Jiafeng (Camilla) Yu, Marc A. Smith

Twitter conversations reflect the social aspects of information diffusion through conventions such as retweets and other conversational responses, through the membership and social distance of these conversations, and in changes in frequency and form of the networks. This project tracked and analyzed Twitter conversations related to energy efficiency and climate change to gain insights about consumer attitudes and behavior. Our broad research question asked, “How can social media reflect consumer sentiment about energy and campaigns to reduce residential use of energy?”

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Publications and Presentations

Semantic analysis of energy-related conversations in social media
In L. Kahle and E.G. Atay (eds.), Sustainability and Lifestyle Marketing, M. E. Sharpe: Armonk, NY.
Russell, M.G., Flora, J., Strohmaier, M., Pöeschke, J., Yu, J., Rubens, N., and Smith, Marc A. (2013)

Future Work

Based on our initial results, the investigators recommend continued collection of data and development of analytical methods and tools that can: track public opinion related to energy consumption; analyze domain-specific, user generated content on social media platforms; identify and track indicators such as semantics and social roles; identify and explore patterns and disruptions; identify and benchmark grassroots resources such as author networks; characterize opportunities for resource transformation; and build semantic models to understand the aggregations of conversation streams. The Twitter Energy data is available for other researchers.

Self-organizing communities of consumers share many of the characteristics of issue publics, and further research on similarities and differences to other issue publics is needed in order to understand how to create, grow and sustain word-of-mouth persuasion for energy behavior change.